Overview

Dataset statistics

Number of variables16
Number of observations3476
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory434.6 KiB
Average record size in memory128.0 B

Variable types

Numeric12
Categorical4

Alerts

green_cover_percentage is highly overall correlated with urban_sustainability_scoreHigh correlation
urban_sustainability_score is highly overall correlated with green_cover_percentageHigh correlation
building_density has unique valuesUnique
road_connectivity has unique valuesUnique
public_transport_access has unique valuesUnique
green_cover_percentage has unique valuesUnique
carbon_footprint has unique valuesUnique
crime_rate has unique valuesUnique
avg_income has unique valuesUnique
renewable_energy_usage has unique valuesUnique
disaster_risk_index has unique valuesUnique
urban_sustainability_score has unique valuesUnique

Reproduction

Analysis started2025-11-25 21:44:30.302919
Analysis finished2025-11-25 21:44:42.876325
Duration12.57 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

building_density
Real number (ℝ)

Unique 

Distinct3476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4963454
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:42.976539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.050621474
Q10.24973999
median0.4954391
Q30.74567135
95-th percentile0.94947314
Maximum1
Range1
Interquartile range (IQR)0.49593136

Descriptive statistics

Standard deviation0.28777043
Coefficient of variation (CV)0.57977859
Kurtosis-1.1937939
Mean0.4963454
Median Absolute Deviation (MAD)0.24714864
Skewness0.00546964
Sum1725.2966
Variance0.082811823
MonotonicityNot monotonic
2025-11-25T21:44:43.106495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.36791472421
 
< 0.1%
0.41430618441
 
< 0.1%
0.82790207161
 
< 0.1%
0.73393590241
 
< 0.1%
0.76964262391
 
< 0.1%
0.01102463411
 
< 0.1%
0.41633134931
 
< 0.1%
0.48155110811
 
< 0.1%
0.019189352721
 
< 0.1%
0.25991955971
 
< 0.1%
Other values (3466)3466
99.7%
ValueCountFrequency (%)
01
< 0.1%
1.909275682 × 10-51
< 0.1%
0.00012311413451
< 0.1%
0.00021550128571
< 0.1%
0.00058657034221
< 0.1%
0.00064204745151
< 0.1%
0.00093220366891
< 0.1%
0.0011009592011
< 0.1%
0.0013426004091
< 0.1%
0.0014628513651
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99933460671
< 0.1%
0.99878925851
< 0.1%
0.99826236371
< 0.1%
0.99819086451
< 0.1%
0.99806971791
< 0.1%
0.99767914311
< 0.1%
0.99762357191
< 0.1%
0.99739052961
< 0.1%
0.99696722891
< 0.1%

road_connectivity
Real number (ℝ)

Unique 

Distinct3476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49020495
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:43.190789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.051348155
Q10.2416857
median0.48317621
Q30.73419042
95-th percentile0.94765583
Maximum1
Range1
Interquartile range (IQR)0.49250472

Descriptive statistics

Standard deviation0.28748364
Coefficient of variation (CV)0.586456
Kurtosis-1.1791254
Mean0.49020495
Median Absolute Deviation (MAD)0.24537602
Skewness0.058398775
Sum1703.9524
Variance0.08264684
MonotonicityNot monotonic
2025-11-25T21:44:43.503691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.39382144511
 
< 0.1%
0.60844515621
 
< 0.1%
0.44467949031
 
< 0.1%
0.74270848541
 
< 0.1%
0.22826837091
 
< 0.1%
0.058262446111
 
< 0.1%
0.29992242931
 
< 0.1%
0.47443787531
 
< 0.1%
0.16791007171
 
< 0.1%
0.35483316971
 
< 0.1%
Other values (3466)3466
99.7%
ValueCountFrequency (%)
01
< 0.1%
1.931256383 × 10-61
< 0.1%
0.00016066471071
< 0.1%
0.00016941695411
< 0.1%
0.00047622563161
< 0.1%
0.00054893475481
< 0.1%
0.0010442356831
< 0.1%
0.0011882656261
< 0.1%
0.0021081141941
< 0.1%
0.0027820375611
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99988953321
< 0.1%
0.999746421
< 0.1%
0.99944358451
< 0.1%
0.99933173331
< 0.1%
0.99909942281
< 0.1%
0.99821018561
< 0.1%
0.99796066671
< 0.1%
0.99738656391
< 0.1%
0.99722042791
< 0.1%

public_transport_access
Real number (ℝ)

Unique 

Distinct3476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50139719
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:43.592361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.047907428
Q10.25609265
median0.49916793
Q30.75205094
95-th percentile0.94527923
Maximum1
Range1
Interquartile range (IQR)0.49595828

Descriptive statistics

Standard deviation0.28716266
Coefficient of variation (CV)0.5727249
Kurtosis-1.1867361
Mean0.50139719
Median Absolute Deviation (MAD)0.24709994
Skewness-0.0051865232
Sum1742.8566
Variance0.082462391
MonotonicityNot monotonic
2025-11-25T21:44:43.678843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.42812905261
 
< 0.1%
0.65848093011
 
< 0.1%
0.26385341251
 
< 0.1%
0.012021672061
 
< 0.1%
0.5190815921
 
< 0.1%
0.60593668911
 
< 0.1%
0.96318331231
 
< 0.1%
0.5896418171
 
< 0.1%
0.47129864761
 
< 0.1%
0.57356653731
 
< 0.1%
Other values (3466)3466
99.7%
ValueCountFrequency (%)
01
< 0.1%
0.00010495748991
< 0.1%
0.00018811899961
< 0.1%
0.00085333225721
< 0.1%
0.00089055366871
< 0.1%
0.0020888853991
< 0.1%
0.0024026716181
< 0.1%
0.0025803081881
< 0.1%
0.0030699898591
< 0.1%
0.0033362875771
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99983192071
< 0.1%
0.99966178921
< 0.1%
0.99907647861
< 0.1%
0.99894371291
< 0.1%
0.99893781861
< 0.1%
0.99888662061
< 0.1%
0.99874755321
< 0.1%
0.99824126151
< 0.1%
0.99811046531
< 0.1%

air_quality_index
Real number (ℝ)

Distinct499
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49444459
Minimum0
Maximum1
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:43.763612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.046092184
Q10.24649299
median0.48897796
Q30.74549098
95-th percentile0.94789579
Maximum1
Range1
Interquartile range (IQR)0.498998

Descriptive statistics

Standard deviation0.28718516
Coefficient of variation (CV)0.58082376
Kurtosis-1.1832201
Mean0.49444459
Median Absolute Deviation (MAD)0.24849699
Skewness0.015452612
Sum1718.6894
Variance0.082475317
MonotonicityNot monotonic
2025-11-25T21:44:43.850038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.222444889817
 
0.5%
0.537074148314
 
0.4%
0.422845691414
 
0.4%
0.490981963914
 
0.4%
0.434869739514
 
0.4%
0.0280561122214
 
0.4%
0.440881763514
 
0.4%
0.290581162314
 
0.4%
0.797595190413
 
0.4%
0.899799599213
 
0.4%
Other values (489)3335
95.9%
ValueCountFrequency (%)
06
0.2%
0.0020040080169
0.3%
0.0040080160328
0.2%
0.00601202404812
0.3%
0.0080160320645
0.1%
0.0100200400811
0.3%
0.01202404817
0.2%
0.014028056115
0.1%
0.016032064137
0.2%
0.018036072149
0.3%
ValueCountFrequency (%)
15
 
0.1%
0.9979959923
 
0.1%
0.9959919849
0.3%
0.9939879767
0.2%
0.99198396795
 
0.1%
0.98997995997
0.2%
0.98797595197
0.2%
0.985971943913
0.4%
0.98396793599
0.3%
0.98196392796
0.2%

green_cover_percentage
Real number (ℝ)

High correlation  Unique 

Distinct3476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50557905
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:43.934588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.046079269
Q10.25676098
median0.51360553
Q30.75763665
95-th percentile0.9484621
Maximum1
Range1
Interquartile range (IQR)0.50087567

Descriptive statistics

Standard deviation0.2883946
Coefficient of variation (CV)0.57042436
Kurtosis-1.1840865
Mean0.50557905
Median Absolute Deviation (MAD)0.24885221
Skewness-0.044935447
Sum1757.3928
Variance0.083171447
MonotonicityNot monotonic
2025-11-25T21:44:44.018864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23855451451
 
< 0.1%
0.91768139421
 
< 0.1%
0.07788275081
 
< 0.1%
0.42730266191
 
< 0.1%
0.53232063571
 
< 0.1%
0.89873504981
 
< 0.1%
0.99777066141
 
< 0.1%
0.75338248941
 
< 0.1%
0.97085661561
 
< 0.1%
0.78710655591
 
< 0.1%
Other values (3466)3466
99.7%
ValueCountFrequency (%)
01
< 0.1%
5.729619626 × 10-51
< 0.1%
7.773023549 × 10-51
< 0.1%
0.00030182898221
< 0.1%
0.00060143999521
< 0.1%
0.00089378896771
< 0.1%
0.0010677643231
< 0.1%
0.001173658321
< 0.1%
0.0014728501661
< 0.1%
0.0031274169461
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99988027811
< 0.1%
0.99953408771
< 0.1%
0.99922448591
< 0.1%
0.99872263961
< 0.1%
0.99834360671
< 0.1%
0.99824933751
< 0.1%
0.99795921281
< 0.1%
0.99777066141
< 0.1%
0.99739493131
< 0.1%

carbon_footprint
Real number (ℝ)

Unique 

Distinct3476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50965726
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:44.108109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.055262007
Q10.26928702
median0.50702976
Q30.75521027
95-th percentile0.95503076
Maximum1
Range1
Interquartile range (IQR)0.48592325

Descriptive statistics

Standard deviation0.2881386
Coefficient of variation (CV)0.56535759
Kurtosis-1.1788428
Mean0.50965726
Median Absolute Deviation (MAD)0.2437702
Skewness-0.025621907
Sum1771.5686
Variance0.083023854
MonotonicityNot monotonic
2025-11-25T21:44:44.191566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.92194958241
 
< 0.1%
0.178213231
 
< 0.1%
0.29515933951
 
< 0.1%
0.97590005941
 
< 0.1%
0.37085585781
 
< 0.1%
0.021966274971
 
< 0.1%
0.14714077061
 
< 0.1%
0.025746123791
 
< 0.1%
0.9900443351
 
< 0.1%
0.2784653331
 
< 0.1%
Other values (3466)3466
99.7%
ValueCountFrequency (%)
01
< 0.1%
0.00038764404431
< 0.1%
0.00063227171611
< 0.1%
0.0014700921781
< 0.1%
0.0019115414641
< 0.1%
0.0021409451191
< 0.1%
0.002557572091
< 0.1%
0.0027527807671
< 0.1%
0.0027744050241
< 0.1%
0.002994821491
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.9994276141
< 0.1%
0.99907083591
< 0.1%
0.99895384691
< 0.1%
0.99882194281
< 0.1%
0.99837915221
< 0.1%
0.99803643011
< 0.1%
0.99802495261
< 0.1%
0.99782849621
< 0.1%
0.99747123921
< 0.1%

population_density
Real number (ℝ)

Distinct3061
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50215805
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:44.287385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.050531181
Q10.25441501
median0.50562224
Q30.75037942
95-th percentile0.94691639
Maximum1
Range1
Interquartile range (IQR)0.4959644

Descriptive statistics

Standard deviation0.28691178
Coefficient of variation (CV)0.57135752
Kurtosis-1.1906657
Mean0.50215805
Median Absolute Deviation (MAD)0.24744757
Skewness-0.01943774
Sum1745.5014
Variance0.082318367
MonotonicityNot monotonic
2025-11-25T21:44:44.539431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.70074503314
 
0.1%
0.073468543054
 
0.1%
0.57188189853
 
0.1%
0.51766004423
 
0.1%
0.072640728483
 
0.1%
0.43446467993
 
0.1%
0.69674392943
 
0.1%
0.71806015453
 
0.1%
0.27221302433
 
0.1%
0.72199227373
 
0.1%
Other values (3051)3444
99.1%
ValueCountFrequency (%)
01
< 0.1%
6.898454746 × 10-51
< 0.1%
0.00055187637971
< 0.1%
0.0010347682121
< 0.1%
0.0016556291391
< 0.1%
0.0017246136871
< 0.1%
0.0017935982341
< 0.1%
0.0020005518761
< 0.1%
0.0030353200881
< 0.1%
0.0033802428261
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99993101551
< 0.1%
0.99986203091
< 0.1%
0.99944812361
< 0.1%
0.99896523181
< 0.1%
0.998482342
0.1%
0.99799944811
< 0.1%
0.9978614791
< 0.1%
0.99779249451
< 0.1%
0.99758554081
< 0.1%

crime_rate
Real number (ℝ)

Unique 

Distinct3476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49965919
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:44.655739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.056823658
Q10.25569733
median0.49904509
Q30.73929325
95-th percentile0.952198
Maximum1
Range1
Interquartile range (IQR)0.48359592

Descriptive statistics

Standard deviation0.28455273
Coefficient of variation (CV)0.56949365
Kurtosis-1.1600715
Mean0.49965919
Median Absolute Deviation (MAD)0.24163972
Skewness0.016448579
Sum1736.8153
Variance0.080970258
MonotonicityNot monotonic
2025-11-25T21:44:44.766498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54715975361
 
< 0.1%
0.76404244761
 
< 0.1%
0.57573647551
 
< 0.1%
0.82672653191
 
< 0.1%
0.12754245631
 
< 0.1%
0.43578911991
 
< 0.1%
0.6097071011
 
< 0.1%
0.6763375151
 
< 0.1%
0.69613532121
 
< 0.1%
0.72933634491
 
< 0.1%
Other values (3466)3466
99.7%
ValueCountFrequency (%)
01
< 0.1%
8.468640386 × 10-51
< 0.1%
0.00098195702681
< 0.1%
0.0013378713251
< 0.1%
0.0013409694391
< 0.1%
0.0015432744521
< 0.1%
0.0015958524981
< 0.1%
0.0019725818781
< 0.1%
0.0028717935281
< 0.1%
0.0031397677921
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99982588811
< 0.1%
0.99934189371
< 0.1%
0.99893365351
< 0.1%
0.99867872131
< 0.1%
0.99853114171
< 0.1%
0.99850520641
< 0.1%
0.99811961691
< 0.1%
0.99809598251
< 0.1%
0.99805388631
< 0.1%

avg_income
Real number (ℝ)

Unique 

Distinct3476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49155569
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:44.875585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.047487835
Q10.24224419
median0.49645737
Q30.73065557
95-th percentile0.94382446
Maximum1
Range1
Interquartile range (IQR)0.48841137

Descriptive statistics

Standard deviation0.28548412
Coefficient of variation (CV)0.58077677
Kurtosis-1.180284
Mean0.49155569
Median Absolute Deviation (MAD)0.24179979
Skewness0.018144268
Sum1708.6476
Variance0.081501185
MonotonicityNot monotonic
2025-11-25T21:44:44.971324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9324121771
 
< 0.1%
0.90324135911
 
< 0.1%
0.20809814291
 
< 0.1%
0.25895092121
 
< 0.1%
0.18039434461
 
< 0.1%
0.19601340621
 
< 0.1%
0.90812701571
 
< 0.1%
0.93141283961
 
< 0.1%
0.051794349621
 
< 0.1%
0.59373324281
 
< 0.1%
Other values (3466)3466
99.7%
ValueCountFrequency (%)
01
< 0.1%
0.00011579496791
< 0.1%
0.00012020338631
< 0.1%
0.00021932343381
< 0.1%
0.0011182202011
< 0.1%
0.0019327121291
< 0.1%
0.0020495253561
< 0.1%
0.0028101260781
< 0.1%
0.002987021881
< 0.1%
0.0031471383631
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99990273111
< 0.1%
0.9998131061
< 0.1%
0.9997148561
< 0.1%
0.99944122691
< 0.1%
0.99923780041
< 0.1%
0.99872080481
< 0.1%
0.99824405271
< 0.1%
0.99804797321
< 0.1%
0.99772979511
< 0.1%

renewable_energy_usage
Real number (ℝ)

Unique 

Distinct3476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49988076
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:45.083887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.052671125
Q10.24510742
median0.49826447
Q30.75226283
95-th percentile0.95181186
Maximum1
Range1
Interquartile range (IQR)0.50715542

Descriptive statistics

Standard deviation0.29014559
Coefficient of variation (CV)0.58042959
Kurtosis-1.2225661
Mean0.49988076
Median Absolute Deviation (MAD)0.25362447
Skewness0.01057443
Sum1737.5855
Variance0.084184462
MonotonicityNot monotonic
2025-11-25T21:44:45.170514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.38235784271
 
< 0.1%
0.37716659741
 
< 0.1%
0.23794899531
 
< 0.1%
0.87653855471
 
< 0.1%
0.67900598551
 
< 0.1%
0.27996571721
 
< 0.1%
0.24213447811
 
< 0.1%
0.017891974561
 
< 0.1%
0.34663102081
 
< 0.1%
0.52688050841
 
< 0.1%
Other values (3466)3466
99.7%
ValueCountFrequency (%)
01
< 0.1%
0.00033230640281
< 0.1%
0.00074737034021
< 0.1%
0.0013979643191
< 0.1%
0.0014568848951
< 0.1%
0.0018951563251
< 0.1%
0.0040470213681
< 0.1%
0.0042802418111
< 0.1%
0.0045220485391
< 0.1%
0.0053976370351
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99996511371
< 0.1%
0.99908395371
< 0.1%
0.99904800211
< 0.1%
0.99894011461
< 0.1%
0.99887712921
< 0.1%
0.99871371081
< 0.1%
0.99864897771
< 0.1%
0.99801030981
< 0.1%
0.99769111481
< 0.1%

disaster_risk_index
Real number (ℝ)

Unique 

Distinct3476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49400249
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:45.421675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.049594006
Q10.24349357
median0.49368825
Q30.73916262
95-th percentile0.94691578
Maximum1
Range1
Interquartile range (IQR)0.49566905

Descriptive statistics

Standard deviation0.2877195
Coefficient of variation (CV)0.58242522
Kurtosis-1.203131
Mean0.49400249
Median Absolute Deviation (MAD)0.24816244
Skewness0.035397822
Sum1717.1526
Variance0.082782514
MonotonicityNot monotonic
2025-11-25T21:44:45.514558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44624170761
 
< 0.1%
0.9368240671
 
< 0.1%
0.46305437491
 
< 0.1%
0.20429334881
 
< 0.1%
0.15142065931
 
< 0.1%
0.99402114881
 
< 0.1%
0.33392930861
 
< 0.1%
0.16008209281
 
< 0.1%
0.0084047110181
 
< 0.1%
0.28562577211
 
< 0.1%
Other values (3466)3466
99.7%
ValueCountFrequency (%)
01
< 0.1%
9.205278477 × 10-51
< 0.1%
0.00017454003761
< 0.1%
0.00087637967491
< 0.1%
0.0010218228171
< 0.1%
0.0012448599381
< 0.1%
0.0017667965651
< 0.1%
0.0018642114311
< 0.1%
0.0021550332911
< 0.1%
0.0022152641971
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99945108981
< 0.1%
0.99918019321
< 0.1%
0.99904818371
< 0.1%
0.99870667341
< 0.1%
0.99836216221
< 0.1%
0.99766429041
< 0.1%
0.99712900771
< 0.1%
0.99676399011
< 0.1%
0.99665632281
< 0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size169.9 KiB
0
2599 
1
877 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3476
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02599
74.8%
1877
 
25.2%

Length

2025-11-25T21:44:45.625974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T21:44:45.675350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02599
74.8%
1877
 
25.2%

Most occurring characters

ValueCountFrequency (%)
02599
74.8%
1877
 
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02599
74.8%
1877
 
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02599
74.8%
1877
 
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02599
74.8%
1877
 
25.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size169.9 KiB
0
2618 
1
858 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3476
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02618
75.3%
1858
 
24.7%

Length

2025-11-25T21:44:45.733879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T21:44:45.777121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02618
75.3%
1858
 
24.7%

Most occurring characters

ValueCountFrequency (%)
02618
75.3%
1858
 
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02618
75.3%
1858
 
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02618
75.3%
1858
 
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02618
75.3%
1858
 
24.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size169.9 KiB
0
2635 
1
841 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3476
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
02635
75.8%
1841
 
24.2%

Length

2025-11-25T21:44:45.850261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T21:44:45.893004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02635
75.8%
1841
 
24.2%

Most occurring characters

ValueCountFrequency (%)
02635
75.8%
1841
 
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02635
75.8%
1841
 
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02635
75.8%
1841
 
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02635
75.8%
1841
 
24.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size169.9 KiB
0
2576 
1
900 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3476
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02576
74.1%
1900
 
25.9%

Length

2025-11-25T21:44:45.948795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T21:44:46.009159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02576
74.1%
1900
 
25.9%

Most occurring characters

ValueCountFrequency (%)
02576
74.1%
1900
 
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02576
74.1%
1900
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02576
74.1%
1900
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02576
74.1%
1900
 
25.9%

urban_sustainability_score
Real number (ℝ)

High correlation  Unique 

Distinct3476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48287127
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-11-25T21:44:46.084873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2046313
Q10.36020222
median0.4825175
Q30.60662513
95-th percentile0.76016675
Maximum1
Range1
Interquartile range (IQR)0.24642291

Descriptive statistics

Standard deviation0.16939136
Coefficient of variation (CV)0.35080025
Kurtosis-0.41724027
Mean0.48287127
Median Absolute Deviation (MAD)0.12305815
Skewness-0.0081400572
Sum1678.4605
Variance0.028693434
MonotonicityNot monotonic
2025-11-25T21:44:46.172066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25923902481
 
< 0.1%
0.576923481
 
< 0.1%
0.25284398531
 
< 0.1%
0.40900165181
 
< 0.1%
0.68637627611
 
< 0.1%
0.59963777711
 
< 0.1%
0.75809275751
 
< 0.1%
0.60333815351
 
< 0.1%
0.597146211
 
< 0.1%
0.66669667811
 
< 0.1%
Other values (3466)3466
99.7%
ValueCountFrequency (%)
01
< 0.1%
0.013400873161
< 0.1%
0.027760321961
< 0.1%
0.028191743671
< 0.1%
0.03048651941
< 0.1%
0.037765764731
< 0.1%
0.039390096281
< 0.1%
0.040500211211
< 0.1%
0.047069919531
< 0.1%
0.056864957821
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.98327788451
< 0.1%
0.96859932251
< 0.1%
0.96538924861
< 0.1%
0.963057911
< 0.1%
0.96264274291
< 0.1%
0.95582608011
< 0.1%
0.94169644261
< 0.1%
0.92420971291
< 0.1%
0.92283101411
< 0.1%

Interactions

2025-11-25T21:44:41.627687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:30.948582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.970191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.791354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.873489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.937872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.953640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.815706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.690358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.599662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.678253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.693988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.763108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.025701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.035171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.969593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.950844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.003793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.021937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.885892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.761434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.672031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.750553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.762355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.842314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.098777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.099145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.070742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.027797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.072474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.085155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.954626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.838012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.742981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.822003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.844631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.918159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.170981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.163932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.173090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.098410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.140392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.150655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.028290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.910637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.817628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.895685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.914716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.993566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.243046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.227381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.248693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.180482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.208904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.215769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.098137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.985388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.887358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.027334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.986703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:42.073148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.348403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.314404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.354409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.287799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.291545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.279617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.172441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.058727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.131091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.098652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.063968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:42.144014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.472037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.389007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.435549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.424419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.374873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.343522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.243018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.133654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.202839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.179521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.141203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:42.220585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.597347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.458502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.501991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.500163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.459573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.406728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.317962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.202843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.267850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.266129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.212703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:42.297056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.665493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.522677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.576236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.577336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.534083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.505538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.385351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.265854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.377512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.340815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.286702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:42.369195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.740736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.590974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.647619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.658614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.607465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.572312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.454660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.345054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.448160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.415820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.364601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:42.451374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.829626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.657822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.721292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.729997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.675321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.635832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.532490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.427138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.522115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.523663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.440510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:42.524751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:31.899760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:32.724049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:33.795074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:34.831620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:35.887819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:36.745138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:37.613565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:38.507033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:39.600302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:40.620549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:44:41.510730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-25T21:44:46.245670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
air_quality_indexavg_incomebuilding_densitycarbon_footprintcrime_ratedisaster_risk_indexgreen_cover_percentageland_use_type_Commercialland_use_type_Green Spaceland_use_type_Industrialland_use_type_Residentialpopulation_densitypublic_transport_accessrenewable_energy_usageroad_connectivityurban_sustainability_score
air_quality_index1.000-0.0140.028-0.014-0.007-0.041-0.0100.0000.0000.0000.0280.010-0.0110.017-0.0070.020
avg_income-0.0141.000-0.0070.001-0.001-0.0070.0360.0390.0220.0060.010-0.0050.004-0.020-0.0090.023
building_density0.028-0.0071.000-0.0110.0030.002-0.0100.0000.0000.0000.000-0.0030.0170.016-0.0140.009
carbon_footprint-0.0140.001-0.0111.0000.0210.0090.0100.0640.0000.0000.0180.0040.043-0.027-0.027-0.329
crime_rate-0.007-0.0010.0030.0211.000-0.0090.0130.0370.0000.0000.0320.0110.0050.017-0.023-0.194
disaster_risk_index-0.041-0.0070.0020.009-0.0091.000-0.0220.0020.0350.0000.037-0.0070.006-0.003-0.007-0.339
green_cover_percentage-0.0100.036-0.0100.0100.013-0.0221.0000.0080.0000.0300.032-0.0120.0030.0400.0020.694
land_use_type_Commercial0.0000.0390.0000.0640.0370.0020.0081.0000.3310.3270.3420.0490.0000.0000.0330.000
land_use_type_Green Space0.0000.0220.0000.0000.0000.0350.0000.3311.0000.3220.3370.0360.0000.0000.0000.039
land_use_type_Industrial0.0000.0060.0000.0000.0000.0000.0300.3270.3221.0000.3330.0160.0000.0530.0000.000
land_use_type_Residential0.0280.0100.0000.0180.0320.0370.0320.3420.3370.3331.0000.0210.0000.0270.0000.000
population_density0.010-0.005-0.0030.0040.011-0.007-0.0120.0490.0360.0160.0211.000-0.017-0.009-0.002-0.020
public_transport_access-0.0110.0040.0170.0430.0050.0060.0030.0000.0000.0000.000-0.0171.000-0.0080.0170.194
renewable_energy_usage0.017-0.0200.016-0.0270.017-0.0030.0400.0000.0000.0530.027-0.009-0.0081.000-0.0120.472
road_connectivity-0.007-0.009-0.014-0.027-0.023-0.0070.0020.0330.0000.0000.000-0.0020.017-0.0121.0000.013
urban_sustainability_score0.0200.0230.009-0.329-0.194-0.3390.6940.0000.0390.0000.000-0.0200.1940.4720.0131.000

Missing values

2025-11-25T21:44:42.678380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T21:44:42.799136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

building_densityroad_connectivitypublic_transport_accessair_quality_indexgreen_cover_percentagecarbon_footprintpopulation_densitycrime_rateavg_incomerenewable_energy_usagedisaster_risk_indexland_use_type_Commercialland_use_type_Green Spaceland_use_type_Industrialland_use_type_Residentialurban_sustainability_score
00.3679150.3938210.4281290.4148300.2385550.9219500.1264490.5471600.9324120.3823580.44624200100.259239
10.4143060.6084450.6584810.4909820.9176810.1782130.3251240.7640420.9032410.3771670.93682401000.576923
20.8279020.4446790.2638530.2645290.0778830.2951590.0421500.5757360.2080980.2379490.46305400010.252844
30.7339360.7427080.0120220.4869740.4273030.9759000.6638380.8267270.2589510.8765390.20429300100.409002
40.7696430.2282680.5190820.9458920.5323210.3708560.4299120.1275420.1803940.6790060.15142100100.686376
50.0110250.0582620.6059370.5871740.8987350.0219660.3881070.4357890.1960130.2799660.99402101000.599638
60.4163310.2999220.9631830.9719440.9977710.1471410.0602920.6097070.9081270.2421340.33392900010.758093
70.4815510.4744380.5896420.8677350.7533820.0257460.7426190.6763380.9314130.0178920.16008200010.603338
80.0191890.1679100.4712990.0741480.9708570.9900440.6431430.6961350.0517940.3466310.00840500100.597146
90.2599200.3548330.5735670.7174350.7871070.2784650.9009380.7293360.5937330.5268810.28562610000.666697
building_densityroad_connectivitypublic_transport_accessair_quality_indexgreen_cover_percentagecarbon_footprintpopulation_densitycrime_rateavg_incomerenewable_energy_usagedisaster_risk_indexland_use_type_Commercialland_use_type_Green Spaceland_use_type_Industrialland_use_type_Residentialurban_sustainability_score
34660.9770500.0090080.0136120.1623250.2436510.9222660.2486200.4094330.1046000.8788680.06833000100.429608
34670.3773610.8085470.0776250.1523050.3489590.1958410.3474060.2950670.9501190.0303360.43519710000.342524
34680.8021930.1760910.3917910.1162320.1624010.8842820.0175910.0538760.1824110.8719830.39153700100.435959
34690.4347170.6064490.1539800.4569140.2298770.4005480.0840920.5210080.4144120.6978800.17764200010.461650
34700.8699170.3369440.3769100.1803610.0763410.8327620.9924120.8170380.2151240.1707450.13362101000.176773
34710.1807280.5857020.6283680.7074150.9199500.6715510.0447020.8518290.8605770.0256220.89674600100.380806
34720.9481050.3156590.5473920.7895790.5303320.1754600.7248900.2354850.7495990.2599460.06785410000.620052
34730.2188590.0867660.2312110.0000000.3966190.9181160.8747930.5999270.3535560.9160130.09932701000.497869
34740.3263570.2228830.3439000.5370740.5349520.2450870.1671500.8563360.6820460.6682900.24509500010.572259
34750.7565240.1879640.6142120.5711420.6282470.2437090.9795810.4088850.1843210.9197100.95304000100.630099